2012 Fiscal Year Final Research Report
Search Method for a Singular Model Taking into AccountSingular Regions in a Search Space
| Project/Area Number |
22500212
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Chubu University |
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Principal Investigator |
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| Project Period (FY) |
2010 – 2012
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| Keywords | 機械学習 / 特異モデル / 探索法 / 特異領域 |
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| Research Abstract |
We proposed a new learning method called SSF for multilayer perceptron (MLP), which makes good use of singular regions to stably and successfully find excellent solutions commensurate with the number of hidden units. SSF worked well in our experiments using artificial and real data sets. We also proposed another learning method for MLP which utilizes eigen vector descent, and showed that it moved through flat singular regions to find excellent solutions. Moreover, we got very promising preliminary results that our SSF framework can be applied to learning of complex-valued MLP to find unbounded or periodic solutions.
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